class_docstr: Dataset for NP Semantic Segmentation Model
  
      Args:
          file_path (str): read data from this local file path
  
          train_to_test_ratio (:obj:`float`): the train-to-test ration of the dataset
  
          feature_vec_dim (:obj:`int`): the size of the feature vector for each noun-phrase
code: def __init__(
      self, file_path, train_to_test_ratio=0.8, feature_vec_dim=603
  ):
      self.file_path = file_path
      self.feature_vec_dim = feature_vec_dim
      self.train_to_test_ratio = train_to_test_ratio
      self.is_y_labels = None
      self.y_labels = None
      self.data_set = self.load_data_to_array_iterator()
  
imports: import argparse
  import csv
  import math
  import os
  from multiprocessing import Pool
  import feature_extraction
  import numpy
  from tqdm import tqdm
  from nlp_architect.utils.io import validate_existing_filepath, validate_parent_exists, validate_proxy_path
label_desc: Use image node_id 1 to create a new NpSemanticSegData object  with example: obj = NpSemanticSegData(file_path, train_to_test_ratio, feature_vec_dim)